Web3-AI Track Overview Report: Technical Logic, Scenario Applications, and In-Depth Analysis of Top Projects

With the continued rise of AI narratives, more and more attention is being focused on this track. An in-depth analysis of the technological logic, application scenarios, and representative projects of the Web3-AI track has been conducted to comprehensively present the panorama and development trends of this field.

1. Web3-AI: Analysis of Technical Logic and Emerging Market Opportunities

1.1 The Fusion Logic of Web3 and AI: How to Define the Web-AI Track

In the past year, AI narrative has been exceptionally popular in the Web3 industry, with AI projects emerging like mushrooms after rain. Although many projects involve AI technology, some projects only use AI in certain parts of their products, and the underlying token economics are not substantially related to AI products. Therefore, these types of projects are not included in the discussion of Web3-AI projects in this article.

The focus of this article is on projects that use blockchain to solve production relation issues and AI to address productivity problems. These projects themselves provide AI products while utilizing the Web3 economic model as a tool for production relations, complementing each other. We categorize such projects as the Web3-AI track. In order to help readers better understand the Web3-AI track, we will elaborate on the development process and challenges of AI, as well as how the combination of Web3 and AI perfectly solves problems and creates new application scenarios.

1.2 The Development Process and Challenges of AI: From Data Collection to Model Inference

AI technology is a technology that enables computers to simulate, extend, and enhance human intelligence. It allows computers to perform various complex tasks, from language translation and image classification to facial recognition and autonomous driving applications. AI is changing the way we live and work.

The process of developing artificial intelligence models usually involves the following key steps: data collection and data preprocessing, model selection and tuning, model training and inference. For a simple example, to develop a model for classifying images of cats and dogs, you need to:

1. Data collection and data preprocessing: Collect an image dataset containing cats and dogs, which can use public datasets or collect real data yourself. Then label each image with a category (cat or dog), ensuring the labels are accurate. Convert the images into a format that the model can recognize, and split the dataset into training, validation, and test sets.

2. Model Selection and Tuning: Choose an appropriate model, such as Convolutional Neural Networks (CNN), which is well-suited for image classification tasks. Adjust the model parameters or architecture based on different requirements. Generally speaking, the network depth of the model can be adjusted according to the complexity of the AI task. In this simple classification example, a shallower network depth may be sufficient.

3. Model Training: You can use GPU, TPU, or high-performance computing clusters to train the model, and the training time is affected by the model complexity and computational power.

4. Model Inference: The file that has been trained is usually referred to as model weights. The inference process refers to the use of the already trained model to predict or classify new data. During this process, a test set or new data can be used to evaluate the model's classification performance, typically assessed using metrics such as accuracy, recall, and F1-score to evaluate the effectiveness of the model.

As shown in the figure, after data collection, data preprocessing, model selection and tuning, and training, the trained model will perform inference on the test set to obtain the predicted values P (probability) for cats and dogs, which indicates the probability that the model infers it is a cat or a dog.

Trained AI models can be further integrated into various applications to perform different tasks. In this example, the cat-dog classification AI model can be integrated into a mobile application, where users upload pictures of cats or dogs to obtain classification results.

However, the centralized AI development process has some issues in the following scenarios:

User Privacy: In centralized scenarios, the development process of AI is often opaque. User data may be stolen and used for AI training without their knowledge.

Data source acquisition: Small teams or individuals may face limitations due to the lack of open-source data when acquiring data in specific fields (such as medical data).

Model selection and tuning: It is difficult for small teams to access specific domain model resources or spend a lot of cost on model tuning.

Acquiring computing power: For individual developers and small teams, the high cost of purchasing GPUs and the expenses of cloud computing power rental can pose a significant financial burden.

AI Asset Income: Data labeling workers often struggle to earn an income that matches their efforts, while the research results of AI developers also find it difficult to match with buyers in need.

The challenges existing in the centralized AI scenario can be addressed by integrating with Web3. As a new type of production relationship, Web3 naturally adapts to AI, which represents a new productive force, thereby promoting simultaneous progress in technology and production capacity.

1.3 The Synergy Between Web3 and AI: Role Transformation and Innovative Applications

The combination of Web3 and AI can enhance user sovereignty, providing users with an open AI collaboration platform, allowing them to transition from AI users in the Web2 era to participants, creating AI that everyone can own. At the same time, the integration of the Web3 world and AI technology can spark more innovative application scenarios and gameplay.

Based on Web3 technology, the development and application of AI will usher in a brand-new collaborative economic system. People's data privacy can be protected, and the data crowdsourcing model promotes the advancement of AI models. Numerous open-source AI resources are available for users, and shared computing power can be acquired at a lower cost. With the help of a decentralized collaborative crowdsourcing mechanism and an open AI market, a fair income distribution system can be realized, thus encouraging more people to promote the advancement of AI technology.

In the Web3 scenario, AI can have a positive impact across multiple tracks. For example, AI models can be integrated into smart contracts to enhance work efficiency in various application scenarios, such as market analysis, security detection, social clustering, and many other functions. Generative AI not only allows users to experience the role of an "artist," such as creating their own NFTs using AI technology, but also creates rich and diverse gaming scenarios and interesting interactive experiences in GameFi. Abundant infrastructure provides a smooth development experience, allowing both AI experts and newcomers looking to enter the AI field to find suitable entry points in this world.

2. Interpretation of the Web3-AI Ecological Project Landscape and Architecture

We mainly studied 41 projects in the Web3-AI track and categorized these projects into different tiers. The division logic for each tier is shown in the figure below, including the infrastructure layer, intermediate layer, and application layer, with each tier further divided into different sectors. In the next chapter, we will conduct a Depth analysis of some representative projects.

The infrastructure layer encompasses the computing resources and technical architecture that support the entire AI lifecycle, the middle layer includes data management, model development, and verification inference services that connect the infrastructure with applications, while the application layer focuses on various applications and solutions that are directly user-facing.

Infrastructure Layer:

The infrastructure layer is the foundation of the AI lifecycle. This article classifies computing power, AI Chain, and development platforms as part of the infrastructure layer. It is the support of these infrastructures that enables the training and inference of AI models, presenting powerful and practical AI applications to users.

• Decentralized computing network: It can provide distributed computing power for AI model training, ensuring efficient and economical utilization of computing resources. Some projects offer decentralized computing power markets, where users can rent computing power at low costs or share computing power to earn profits, represented by projects such as IO.NET and Hyperbolic. In addition, some projects have derived new ways of play, such as Compute Labs, which proposes a tokenization protocol, allowing users to participate in computing power rental to earn profits in various ways by purchasing NFTs that represent GPU entities.

• AI Chain: Utilizes blockchain as the foundation for the AI lifecycle, achieving seamless interaction between on-chain and off-chain AI resources, and promoting the development of the industry ecosystem. The decentralized AI market on the chain can trade AI assets such as data, models, agents, etc., and provides AI development frameworks and supporting development tools, with representative projects like Sahara AI. AI Chain can also promote technological advancements in AI across different fields, such as Bittensor, which encourages competition among different types of AI subnets through an innovative subnet incentive mechanism.

• Development Platforms: Some projects offer AI agent development platforms that also enable trading with AI agents, such as Fetch.ai and ChainML. One-stop tools help developers more easily create, train, and deploy AI models, represented by projects like Nimble. These infrastructures facilitate the widespread application of AI technology in the Web3 ecosystem.

Middleware:

This layer involves AI data, models, as well as reasoning and verification, and utilizing Web3 technology can achieve higher work efficiency.

• Data: The quality and quantity of data are key factors affecting the model training effectiveness. In the Web3 world, resource utilization can be optimized and data costs can be reduced through crowdsourced data and collaborative data processing. Users can have autonomy over their data and sell their data while protecting their privacy, avoiding theft and excessive profits by malicious merchants. For data demanders, these platforms offer a wide range of choices at extremely low costs. Representative projects such as Grass utilize user bandwidth to scrape web data, while xData collects media information through user-friendly plugins and supports users in uploading tweet information.

In addition, some platforms allow domain experts or ordinary users to perform data preprocessing tasks, such as image labeling and data classification. These tasks may require specialized knowledge for financial and legal data processing, and users can tokenize their skills to achieve collaborative crowdsourcing of data preprocessing. For example, AI markets like Sahara AI have data tasks from different fields that can cover multi-domain data scenarios; while AIT Protocol labels data through human-machine collaboration.

• Model: As mentioned earlier in the AI development process, different types of requirements need to match suitable models. Common models for image tasks include CNN and GAN, while for object detection tasks, the Yolo series can be chosen. For text-related tasks, common models include RNN and Transformer, as well as some specific or general large models. The depth of the models required for tasks of varying complexity is also different, and sometimes model tuning is necessary.

Some projects support users in providing different types of models or collaborative training of models through crowdsourcing. For example, Sentient allows users to place trusted model data in the storage layer and distribution layer for model optimization through its modular design. The development tools provided by Sahara AI come with advanced AI algorithms and computing frameworks, and they have collaborative training capabilities.

• Inference and Verification: After the model is trained, it generates a model weight file that can be used for direct classification, prediction, or other specific tasks. This process is called inference. The inference process is usually accompanied by a verification mechanism to validate whether the source of the inference model is correct and whether there are any malicious behaviors. In Web3, inference can often be integrated into smart contracts, allowing for inference by calling the model. Common verification methods include technologies like ZKML, OPML, and TEE. Representative projects such as the ORA chain AI oracle (OAO) have introduced OPML as a verifiable layer for AI oracles. The ORA official website also mentions their research on ZKML and opp/ai (combining ZKML with OPML).

Application Layer:

This layer mainly consists of user-facing applications that combine AI with Web3 to create more interesting and innovative gameplay. This article primarily organizes projects in several areas: AIGC (AI-generated content), AI agents, and data analysis.

• AIGC: Through AIGC, it can be extended to NFT, games, and other tracks in Web3. Users can directly generate text, images, and audio through Prompts (the prompts given by users), and even create custom gameplay in games according to their preferences. NFT projects like NFPrompt allow users to generate NFTs through AI for trading in the market; games like Sleepless enable users to shape the personality of virtual companions through dialogue to match their preferences.

• AI Agent: Refers to an artificial intelligence system that can autonomously execute tasks and make decisions. AI agents typically possess the abilities of perception, reasoning, learning, and action, allowing them to perform complex tasks in various environments. Common AI agents include language translation.